{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入所需的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division,print_function,unicode_literals\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display,HTML\n",
    "%matplotlib inline\n",
    "#设置参数\n",
    "\n",
    "\n",
    "pd.options.display.max_colwidth=600\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据集\n",
    "\n",
    "\n",
    "Instant记录号\n",
    "Dteday：日期\n",
    "Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "yr：年份，(0: 2011, 1:2012)\n",
    "mnth：月份( 1 to 12)\n",
    "hr：小时 (0 to 23) （只在hour.csv有，作业忽略此字段）\n",
    "holiday：是否是节假日（0/1）\n",
    "weekday：星期中的哪天，取值为0～6\n",
    "workingday：是否工作日（0/1）\n",
    "1=工作日 （是否为工作日，1为工作日，0为非周末或节假日）\n",
    "weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）\n",
    "temp：气温摄氏度\n",
    "atemp：体感温度\n",
    "hum：湿度\n",
    "windspeed：风速\n",
    "casual：非注册用户贡献的骑行量（作业无需理会该字段）\n",
    "registered：注册用户贡献的骑行量（作业无需理会该字段）\n",
    "cnt：给定日期（天, day.csv）时间（每小时,hour.csv）总租车人数，响应变量y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集的大小::(731, 16)\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv('day.csv')\n",
    "print('数据集的大小::{}'.format(data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[dtype('int64'),\n",
       " dtype('O'),\n",
       " dtype('int64'),\n",
       " dtype('int64'),\n",
       " dtype('int64'),\n",
       " dtype('int64'),\n",
       " dtype('int64'),\n",
       " dtype('int64'),\n",
       " dtype('int64'),\n",
       " dtype('float64'),\n",
       " dtype('float64'),\n",
       " dtype('float64'),\n",
       " dtype('float64'),\n",
       " dtype('int64'),\n",
       " dtype('int64'),\n",
       " dtype('int64')]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标准化属性名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns={\n",
    "    'instant':'rec_id',\n",
    "    'dteday':'datetime',\n",
    "    'holiday':'is_holiday',\n",
    "    'workingday':'is_workingday',\n",
    "    'weathersit':'weather_condition',\n",
    "    'hum':'humidity',\n",
    "    'mnth':'month',\n",
    "    'cnt':'total_count',\n",
    "    'hr':'hour',\n",
    "    'yr':'year'\n",
    "},inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 类型转换属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#日期时间转换\n",
    "data['datetime']=pd.to_datetime(data.datetime)\n",
    "data['season']=data.season.astype('category')\n",
    "data['is_holiday']=data.is_holiday.astype('category')\n",
    "data['weekday']=data.weekday.astype('category')\n",
    "data['weather_condition']=data.weather_condition.astype('category')\n",
    "data['is_workingday']=data.is_workingday.astype('category')\n",
    "data['month']=data.month.astype('category')\n",
    "data['year']=data.year.astype('category')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可视化属性，趋势和属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x7fa02fb18ba8>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3CqZZArwI3FT26EREpGp1d7v+ewm3gcHMFgBXuvvrlQhMRESqWyndlM8GMLO1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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa02f9e4ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "sn.boxplot(data=data[['season','total_count']],x='season',y='total_count',ax=ax)\n",
    "ax.set(title='Box Pot for season distribution of counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x7fa02f9a0b00>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9FY4XUyXj1fJrG9zx1q9wvAkVizeWjaPEKhRvkx72ixkvX39LMCOBRXnblgCj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa02fd02eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "sn.barplot(data=data[['month','total_count']],x='month',y='total_count')\n",
    "ax.set(title='Montly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LLbeQnn5ySIDDhw+za9cuevfuHaoJwzBiJNIy2mrgZdw5mt8B7wJbgsrk4g6W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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa02fa15588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_col_list=['month','weekday','total_count']\n",
    "plot_col_list=['month','total_count']\n",
    "spring_df=data[data['season']==1][df_col_list]\n",
    "fig,ax=plt.subplots(nrows=2,ncols=2)\n",
    "\n",
    "c=0\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        c+=1\n",
    "        if c==1:\n",
    "            title='Spring'\n",
    "        elif c==2:\n",
    "            title='Summer'\n",
    "        elif c==3:\n",
    "            title='Fall'\n",
    "        else :\n",
    "            title='Winter'\n",
    "        sn.barplot(data=data[data['season']==c][plot_col_list],x='month',y='total_count',ax=ax[i][j])\n",
    "        ax[i][j].set(title=title)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa02fcd8518>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6KsjLy+OLL77giy++ICIigqFDhzJy5EiGDRtGu3Zt8O+rBV1wwQVMmzaNBQsW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\nMIAkSVkYQJKkLP4D6K4abGZSnUkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa02f9e4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=data[['year','total_count']],x='year',y='total_count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa016169320>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jwoQJeLq4UekvEmnkT1S/urIFBQU4jsPKlStpbGxk0KBBjBs3jhdffJFCL5y8\nq49Cn4fBxR5+OsbHC5+FACjzezj0ez5K/R4KvB5OGuMj5MCqWtuzqcBnJ8UKbgri8XooGFKAt8T+\ntwWWBygZX4JvoA9PgYeyiWUENwXtHaFr3+JivucvxOvxsH9JKetDIRpCofRfoD4q8jpUFm3p01VZ\nFKTIG7v3V6bNnj2bcePGceSRRzJkyBAmTZrEypUrGTVq1OZ93AFjVwFjRGQwsC128Naw97EDuOL+\n+36stPHmKRAI9PonGAziOA4ff/wxGzZsoLi4GBHh+eef71PZJoNluy/XI9bPxo0bWbNmzebjr1mz\nho0bN3baL1d197d3R7vIJMl2223H9ddfzyOPPMJll13GLrvswowZM/jqq6/4qh6O/fuWmnwHCLmf\nsZZ2h7veDbLs6xCbAuDxQFMb1LnDZv1yDx+vfOJl04JNEITCnQopm1CGp8BDqCGEb+CWO25PgQdv\nmZdQw5YP2UDvlu2F7t1UwAlRntJ7i+TzeOCUqnru+8SOXXpKVT3ZenM4e/ZsZs2axbvvvssbb7xB\nYWEhTU1NlJaWRu8aHoy1PGI5ehvu9ujZYhMayLWv46CdffbZ3HfffVx++eVsv/32nHDCCfzrX//q\nU9ku27+MpjeaMlK2kz0uXEtL53Huli9fTlFRxyq7/jbwrQaYJDr88MM5/PDDaW1t5eGHH2b69On8\n4Q9/4PuDPdx2ZOzhRh5bEeLjdQ7XH+pnmPuW0XGPthG+Nx9U7KF8kv3+CdYF2bRwE81lzZTuWYq3\n3EtwUxD/NvbYTtAh1BjCW55bwSNeu2/Vys0T1mc6G3Hx+XzstddePP300zzyyCOUlpZSVxc92Pfm\nwVgbIpYDUdsgCQO59nXg0jFjxnD66afT1tbG/Pnzufnmm7n88st567m/9bpse0u8GSvbyR7Itbm5\nudO60aNHd5puOx8Hvu0u+OTnN1EG/Otf/+KVV16hubkZv99PWVkZPp+Pgw8+mPpWh8eXBwm0O7Ye\nv8Hhza/tnVhTm4PfCxVF0BaC+94P0hzRLeKlz4MEN9kqCk+hB7xsrtct3qWY5rea7fZ2h8bqRnwV\nPgqG6X1DtggGg3zxxRdUVVXxxRdfbF7vzqO0I7ZtpRY7Mvm4iKTdDeS6OW28+SguLu71z9dff82b\nb76J4ziUl5czcOBAfD4fU6ZM6VPZblnZkrGy3Zfr0dVPPOfIVYn83ZH0myhJ2trauOWWW1i5ciUe\nj4cddtgDwf6eAAAgAElEQVSBuXPnUlpayvWH+rnr3SB//7iNQBCGlXn4UZWN7Sfu4uPTDe385Ik2\nyv1w4mgfQyJqUj6tdah7oo5QIISn0EPRjkUU72b/U0v2LMFpd6h7sm5zT5uKoys2N4Kq9Fq/fj2v\nv/46Bx98MMXFxVRXV7Nw4UJuuOEG9txzT+bMmYOInAAsBC4HPohopH8AuExE3sJO+f0L4HR325PA\n9d2kTalUle32te00vtaoZTuPaYBJEhFh/vz5MbcNL/cw68DYl3pwiYc/HtqxiuGE0Vvqls/co4B/\n/jC6dsTy+DyUTSijbELsEXwGHT+IKRHDzw/werlz2PBu/w7Vex6Ph0ceeYQrrriCUCjEiBEjuOSS\nS5g8eTIA559/PnPmzLkaeBD7LstJEcmvwL4HsxpoBv5ojFkEYIxZ6waXW7pIm1KpKttlE8s2T5MQ\nTct2fkhVgAlh3/aPaxIypXJFaWkpXq8XYnRgq6ys5MEHH+wy7dixYzHG7BJrmzGmBTjD/Ym1/QUg\nZlqlslW3AcZ9oTJu4ZcujTEOWx7vlcoqXQ1uCdDidN4WbhfweDxUVrpDx7T1m/eHleq1np5gGoh5\nr9alrt9SUypLrL9jfZdvaZ0Xo7TP06oXpXqlpwBzBokFGKWyXvmh5V1uO/FNvUdS3Rv/2wc6rwy2\nMShq1QGXPgy+jm1QT8b95lJ+6DbAGGPuS1M+lEqb4tFdd62c+FHnj0Qig10qpbbQ92CUUkqlREK9\nyETkJ9j++TsDnW4DjTE6iqLKOS0rWwjUBAhuDHJBjBmS5wzVYq1Ub8T9BCMi07Bdjz8FRgJPA8+4\nx9iE7aOvVE4JmAD1L9TjG+gj1BBiXFERY4uKCOFQ7PVwSOfxw/qF6upqpk6dytSpU6murs50dlSO\nSuQJ5rfYeSiuxU6RfJsx5h0RGQAsppvh+6OJyIPAoUAZdmbM64wx/+Nu63JiJZ2USSVb87vNlO5d\nSsmeJQRqAkwqLWV7v59AaAA3bazdPIhif6JDz6tkSaQNpgp4zRgTBIK4g+8ZY+qBPwLnJnCsOcAO\nxpgK4D+AP4jI+IiJlWYBlcBb2ImVwmazZVKmQ4CLROQIgDjSKtVJsC5IwfACOwSJ147GC1Ds9XJE\naRkvNsWoM8tzgUCg09DzuTzUvMqcRJ5g6oDw2NNfAaOBl9xlD7BVvAcyxkQO0ue4PzsC4+l+YqWs\nm5QpZpfFTn7bt5NEjJlbPOqOhJN/0trK4qZGvmxrY0MoxDFl5Rxd3nVX3f7EW+i1t0uAt8zLN+1B\npNAuO0BDjBcv+4ONe/68w/IBlz/axZ59KNtRY0Fr2c4/iQSYt4DdgOew7S+Xi0g7thfn5dgqqbiJ\nyG3Y4FACvAs8C1xN1MRKIhKelOk7Yk/KdKz7e6dJmcJpgbgDTD7eqbU4Dtv4CtinuJj59XGP8J6X\n1yJawdAC2te3U7h9IYXfK+SZjxrwAT6Ph2caG/ieP/ZQ9LH0h+uVbXpbtpPOW0DIX4a3zT7xhvxl\n4NWhHhO5AnOwVVNgA8r2wG3Yt/ffBM5K5MTGmOkiMgOYABwMtND9xEopn5QJkj8RUTYY6zZcAzyR\nwIcwk9ciXRMzlYwvIVRvq8VK9y1lO+PwUP0mHGD7Aj8/GxB7oNFY8rHsZLvelu2k83hoGrUvpauX\nAtA0al+ydka8NIo7wBhjXgded3/fCBzjNroXGWM29ebkbnvOqyLyM+Bsup9YKeWTMkFvJiLK3y+V\nZE/KlIh0TczkH+4HdyQYb5GXcwYNps1xaHccSry2iTLeFy17ul4agPJb+8Dt2LTbdpnORlaJO8CI\nyD3AVcaYz8Lr3BFgW0Rke+AKY0zMkWDjzEd4AqXNlb/RkzKJSHhSpsXuLtGTMsVMm0hGcnlSoGTr\nD9ei/p/1lO5diq9iyxAxfo8Hv8fD+mCQBQ0NTBs4MK5j9YfrpVQiEqkiOw34C/BZjG1bY7/cewww\nIjIU+CH2HZpmYDLwU2AaUE33Eytl1aRMTj9tAM4nLctbKN61uEOACWsIhVgaaI47wCilOkp0qJiu\nvlF3pXP7R3fHOBv4N1AL3ACcb4x5yhizFjgB29hfC+xL50mZVmEnZVoCXB85KVMPaZNOG3Xz21ft\n7Qzw6mhKSvVWT/PB/Br4tbvoAP8QkZao3YqxTxP3xXNCNxBM6mZ7lxMr6aRMKhma32+m+f1mu+CB\n+mfrN080cUmjbZhtcxw2hUJMLC7JUC6Vyn09VZF9DDyOfc/lN8CLwDdR+7RiuwH/Pem5U0kRCIVY\nG7Qve7QDdaEQX7a1UeTxMLSg/3Wl9A32UbRjETjQ/F4z/hF+vGX2SWVPYwOMz+NhuK+AvYqLdb6K\nLKZlO7v1NFz/YtwGdRGpB/7HGPNVOjKmkmd1ext/qq3dvPxScxMvNTexs9/PhZVxvx+bNwpHFVI4\nyr5N6Sn0UPSDInzl9hHmxK91uP5comU7uyXSTfm/AUSkEBiLHY5lA/ChMabffgYHvXN/h+VnnnmG\nkpKO1SpfXDm2T+f46eD438WIRQqLuFNnZYypdB87mKUTdAiuD/JxS5Ayr5cRBQUU9OP3GOIp19C3\nst3Xcg1atrNdosP1XwRcjH2/JPzpqxORa4wx1yc7c0qlQ9M7TTS/3YzT6vBntz6sxOPhyLJyDi8r\ny2zm0qDTcEdxzs4I/W+GRpWYRN6DOR/7Nv9fsINIfodt3P8JMEdEWowxc1OSyyyg06Tmp+b3mmla\n2kTxrsUU7VTEBS/72RQK8VYgwJMN9RR44MDS/A8ySqVCIk8w5wDXGmMujVhngJdFZCNwHpC3AUbl\np+YPmynZs4SyCTaIDC8oYDiwc2EhJV4P/9fUxA9Ly6hwHDa5VWYVjkP8I5Qp1X8l0sl/O2wvslhe\nwk5CplROCTWE8I+MHS7EX0htMIgHOCoYotxxKHccjgqG6L+tM0rFL5EnmC+AKcALMbYd5m5XKqd4\ny720fdFG4XaFnbZ93NpKpc/2LtvZcfhNezDd2VMqpyUSYOYCc0WkEngM2wYzFJiKHUbmvKTnTqkU\nKxlXQuPLjYRaQhTtWMQXbQ71oRBvBwJUB5o5KYHRlFtbW5k9ezZLly5l48aNbL/99sycOZNJk+x7\nxR999BHTpk1bgc7WqvqJRLop3+K+xX8F9k16B9uT7GvgV+Epj5XKJSW7leDxeWh6o4mWj1u42l0/\n0Ovl5AEVHFhaGvex2tvb2WabbfjrX//Ktttuy5IlSzj//PNZsGABpaWl3HTTTWBnXF2AnX58PrCf\nm3w2W2ZrHQ68KCIfG2MWRczWemYXaZXKSnG3wYjI5dhBJLfDfggmuP9uByx0t/cv7iRDYTrJUO5p\neqMJ//Z+Bp82mME/H8zvKyuZs/UQ/rj1EMYWFfFMQ0PPB3GVlpYyY8YMRo4cidfr5ZBDDmHkyJHU\n1NSwePFiRo4ciTHmUWNMABtQxolIeGijU7GjldcaY5YD4dlaIWK21i7SKpWVEvk2vAJYZIz5GvjS\n/QFARLZ1t1+Z3OxlOZ1kKOc1vWkDjK/ch2+Aj+/5t3wk6kIhFjQ28KNeTsG7bt06Pv/8c3baaSce\neeQRRo0atXlbumdr1YFZO9LrkZjeXq9EAoyHrkdTHokdwbjf0UmGclw3A43VBoOU9vKGoa2tjQsv\nvJDjjjuOHXfckaamJko7V7elbbZWneysI70eient9eppNOWfs2USLwe4XUSiZ68sxg4d83yvcpDj\nnhyQ+gEMHqndcsl7M7zGc42NvNsS4Nv2dhxg24ICji4rZ1d3qtl0qq6uDrdFMHPmTCZOnJj2PASW\nB2hZ4Q4K7oHGlxrxFNpA8qcN7mjKOHzV3s4PCjv3LutJKBTioosuwu/3M2vWLMBWn9XV1UXvmrbZ\nWrufbbPzl8e9k9ZErbk53lPFLbJcQ3rLdt9ma+1/wam769Vd8OnpCaYJWO/+7sHeNW2I2qcV+F/g\ntp4yqTJjRWsL+xeXsIPfj9/j4ZXmJm7ZWMuFgyvZqRdfoL3lOA7z5s1jwwZbhObNm8eECRPwpLla\n0eP34Cl2z+mAp8iDp8gul3ntvwUeL7sWFnFwAo38YP/GSy+9lHXr1nHXXXfh99t3bKqqqli2bNnm\n/dI9W2tCs23mUDtib8u2zj6amN5er55GU34UeBRARO4FroycMlnlhl8PruywPHVABTWtrbzbEkhr\ngAkEAqxZs+XOeM2aNQQCgZiDKKZS0U5FFO1k73DrX6indJ8tUyb/6tG+fbleccUVrFq1invvvbfD\nh/Kwww5jzpw55MRsrTnUjpgtZVvFFncvMmPM6Rpc8kPIcWgJhSjX2RoZMHlAzOmSe+Orr75i/vz5\nLF++nAMOOIA99tiDPfbYg6effprKykrOP/98yJHZWnOVlu3skjvPwipp/rexkSbHYT+drTGpRowY\ngTGmy+1jx47FGKOztaaQlu3somG+n3mpqYlnGxs4a+AgBvuSc+euVDbQsp190v4E4w6JcRswGTtp\n2afAJcaY/3W3dzkkhg6n0TfPNzbydEMD5wwazA8y0INMqVTRsp2dMvEEU4B9SXMSMBA7dMbfRWSH\niCExZmGDz1vYITHCZrNlOI1DgItE5AiAONL2a0811LOgsYEZg/UDqPKLlu3slfYnGGNMIzZQhD0j\nIp8B44GtcIfEABCR2cA6EdnF7TFzKnC6MaYWqBWR8HAai4gYTqOLtP3W/PpNvNzUxJkDBzHc56Mu\naEcF9ns8lGpjqMphWrazW8Yb+UVkGLAztk//2XQxJEY6htOA/BxC4p9NTQDcXrexw/oJxcWcPjB6\nTs4tkn0tYh0vEAik/T2YVMnHspPtelu2VXpkNMCIiB94CLjfGLNCRLobEiPlw2lA4kMiHFf/2x73\n6evb/r15wznSncOG9ypdsofTaGlp6bRu+fLlFMWo1hiS1DOnRz4NPxJPuYa+le2+lmvofdlW6ZGx\nACMiXuCv2JEAznVXdzckRsqH04DuhkTIny+PePVtOI3OmpubO60bPXp0zBct1yxM6qnToqfrlU8B\nSKl4ZCTAiIgHuBv7xvJRxpg2d1OXQ2KkYzgN0CEkIiX7WjhO55Eli4uL8+aa58vfoVSyZOoJ5nZg\nNDDZGBN5W9vTkBjZM5yGUkqpbqW9m4WIbA+cBewOfCsiDe7PyXEMiaHDaSilVI7IRDfl1diRmbva\n3uWQGDqchlJK5Q7tKK6UUiolNMAopZRKCQ0wSimlUiLjb/Kr/DT+tw90XhlsI/rd6gMufRh8/k67\nPpnQ67FKqWykTzBKKaVSQp9g+qh41B097vPTTgMMpNfS5mb+2dTIumCQNsdhK5+PA0pKOay0NG/G\nAVPJFU+5Bi3bqnsaYPqBCq+Xo8vKGV5QQAGwsq2NhzdtwueBQ0vLMp09pXpNy3Z20wDTD4yJGkxy\nSEEB77UEMK2t+iFUOU3LdnbTNph+xnEcPmtrZVVrG1JYmOnsKJU0Wrazjz7B9BNNoRC/W7eWdsch\nBPy4rFzv8FRe0LKdvTTA9BPFHg+zKrei1XFY1dbGkw31DPR6ObC0NNNZU6pPtGxnLw0w/YTX42Fo\ngf3vHun30+SEeKqxQT+EKudp2c5e2gbTT4UcaI8xP4tSuU7LdvbQJ5h+4OmGenbyFzLE5yMIrGxt\n5bmmRiYWd55JUqlcomU7u2mA6QeaHYeH6jexMRjE7/Gwtc/HceXlTCrRKoRkevDBB3niiSf45JNP\n+NGPfsS11167edvSpUu5+OKL+eabb5qAZcBp7tQViEgRdhK+E4Em4DpjzI3htCJyKHArMCo6bX+n\nZTu7aYDpo8AXZ/W4z5MDru/TOX46uG9vS/9kQAU/0bG9Um7o0KFMnz6dV155hZaWls3rN2zYwLnn\nnssZZ5zB3LlzK4GrgPnAfu4us4EqYHtgOPCiiHxsjFkkIlsDTwBnAgtipE2JeMo19K1s97Vcg5bt\nbKdtMEolyZQpU5g8eTKDBnUc0nPx4sVUVVWx3377YYwJYAPKOBEJT453KnCVMabWGLMcuAs4zd12\nPFBjjHm0i7RKZS19glHp4y0g5C/D29YIQMhfBt78L4IrV65ERDYvG2MaRWQVMEZEvgO2Bd6PSPI+\ncKz7+5jIbZFpgRXx5iEQCPT+D8hDej0S09vrlf+fbpU9PB6aRu1L6eqlADSN2hf6wYCETU1NVFZW\nRq+uAwYA5RHL0dtwt6/tIm3campqEtk97+n1SExvr1dGAoyInIutAhgLPGKMOS1iW5cNmtoYmvva\nB27Hpt22y3Q20qq0tJSGhobo1RVAPdAQsRyI2oa7PbqxInJ7XMaMGdPN1v73Zdv99eiJXq9I3QWf\nTLXBfA38AbgncmVEg+YsoBJ4C9ugGTabLY2hhwAXicgRcaZVKiOqqqpYsWJLbZaIlAE7YttWaoFv\ngHERScax5VusJnJbZNpE8lBcXNzlT3/U3fXo6ac/6u31yEiAMcY8YYz5B7A+alNPDZraGKqyVnt7\nOy0tLYRCIYLBIC0tLbS3t3PYYYexcuVK3njjDUSkGLgc+MAYE446DwCXichgt7z+ArjP3fYksKuI\nnNBFWqWyVrb1IuvUoAmEG0MHE7sxdExPaRPJQCAQiPnTH3V1LeL56Y/mzZvHbrvtxp133snTTz/N\nbrvtxrx58ygtLeVPf/oT8+fPB6gF9gVOikh6BbasrgaWANcbYxYBGGPWAicAV3eRVqmslW2N/N01\naGpjaJrptUjMQQcdxEEHHdRpfU1NDQMGDOBPf/oT48eP7/SKuTGmBTjD/enEGPMCoE/iKudkW4Dp\nrkEzw42h/e/LVhtCE9PT9dKArfqbbAswNcDPwwvRjaEiEm4MXezuEt0YGjNtIhnor414sei1SIxe\nL6U6ylQ35QL33D7A5zZetmMbNK8XkROAhXTdGPoWMAzbGHq6u62ntClx47K7e9znMzq9A5GQayJ+\nv2Rqe5+OpVQ84inX0LeyfU3Uspbt/JOpRv7LgGbg98DP3N8vi6NBUxtDk2BFawtnffctl66LbrJS\nKndpuc4+GXmCMcbMxnYjjrWtywZNbQztu03BIPfW1fGDwkLWBIOZzo5SSaHlOjtlWxuMSqGQ4/A/\nm+o4uLSUNsfRD6LKC1qus1e2vQejUmhhYyMe4PDSskxnRamk0XKdvTTA9BMrWltY0tzEGRUD8faD\nASZV/6DlOrtpgOkH6kMh7qmr4+cVAxno82U6O0olhZbr7KdtMP3A1+1tbAyFuHVj7eZ1jvvzq+++\n5fSKgexbonOYq9yi5Tr7aYDpB3bw+7liq606rHupqYkPWlo4b/BgBnv17k/lHi3X2U8DTD9Q5PEy\noqBjbegAr5cCj4cRBf4M5UqpvtFynf20DUYppVRK6BNMH/1m3//qcZ8nB1zfp3P8dHD0GJ599x/l\nA/iP8oQGmlb9SDzlGvpWtrVc5z99glFKKZUSGmCUUkqlhAYYpZRSKaEBRimlVEpogFFKKZUSGmCU\nUkqlhAYYpZRSKaEBRimlVEpogFFKKZUSGmCUUkqlhAYYpZRSKZF3Y5GJSCVwNzAFWAdcbIx5OLO5\nUqpvtFyrXJSPTzC3Aq3AMOBk4HYRGZPZLCnVZ1quVc7JqycYESkDTgB2NcY0AK+KyNPAKcDv4zlG\nIBBIYQ5zi16LxKTqemm5Tj69Honp7fXyOI6T5KxkjojsAVQbY0oi1l0ITDLG/Lin9G+//Xb+XAyV\nlcaPH+9JNI2Wa5XtuirXefUEA5QDdVHr6oC4JojozYdfqTTQcq1yUr61wTQA0bMYVQD1GciLUsmi\n5VrlpHwLMJ8ABSJSFbFuHFCTofwolQxarlVOyqs2GAAR+RvgAGcCuwPPAhONMfphVDlLy7XKRfn2\nBAMwHSgB1gCPAGfrh1DlAS3XKufk3ROMUkqp7JCPTzBKKaWygAYYpZRSKaEBRimlVEpogFFKKZUS\n+fYmf94QkaOAa4DRwDfAXGPMjZnNVfYSkYOAC7BdeEcBs4wxf8hsrlQsWrbjl+vlWp9gspCI7AU8\nBSzCFqzZwDUi8qtM5ivLlQMfAxcB32Y4L6oLWrYTltPlWp9gstNvgDeNMeGRcpe7Q7P/DvhL5rKV\nvYwxz2JfPkRE/pjh7KiuadlOQK6Xa32CyU77Y+/wIi0CdhCRkRnIj1LJomW7H9EAk522ofPj8LcR\n25TKVVq2+xENMLlHh15Q+UrLdp7RAJOdvgGGR60b5v6bcw19SkXQst2PaIDJTq8Bh0etOwJYbYz5\ndwbyo1SyaNnuR7QXWXa6CagWkauBvwL7ADOAmRnNVRYTkXJgJ3exEBguIrsDDcaYTzOXMxVFy3YC\ncr1c62jKWUpEjsa+jLYLturgz/oyWtdE5GDgxRiblhhjDk5vblR3tGzHL9fLtQYYpZRSKaFtMEop\npVJCA4xSSqmU0ACjlFIqJTTAKKWUSgkNMEoppVJCA4xSSqmU0ACTQ0TkPhF5K0nHckTk3CQcZwf3\nWD+KWPe5iNzQQ7pd3XQH9zUPKrOSWS7TSUQOdsvgrt3sM1tE1qU5XzeIyOfpPGeq6Jv8ueUqoCTT\nmYjDccD6TGdCpU2ulMve+B9gQaYzkas0wOQQY8yqTOchHsaYdzOdB5U+uVIuw0TEAxTFs687PpqO\nkdZLGmByiIjcB+xqjNlLRAYBNwBHAZXAGuA5Y8wvEjikT0SuAX6BHSr9UeA3xpiWiHPuDvwJmAC0\nYGfX+40x5rtu8vk58Jgx5sKIddOBi928/h8wN0a6C4CTgJ2BAPAGMDM85pKInANcC2xjjGmISHeI\ne8xxxpgPEvj7VRIkq1yKyAPAcGPMFHdZgBXAk8aY491144G3gJ2NMSvddecCv8bOWf8lcKsx5qaI\n484GzgWOxY6FthtwprtvdB5OAh4AzjPG/CWc1hiztbv9YOzQLYcA5wBHun/jDcaY26KOdS52ps5K\nYDEwD3gBOMQY85K7zyDgNuAYYBNwS4w8bQNcDRyMnTPnS+DvwJXGmFZ3nzeBj4wxp0elvR8Ya4zZ\ns9MFTwNtg8ldNwIHYAcJPBy4hMTn07gA2Bb4GXA9cBb2gwqAiAwBXgJKgWnYQQknAYtFpDDek4jI\nMcCtwDPA8cCHwD0xdh2J/YAdgw16PuA1ERnobn8Ie1N0YlS604B3NLhkhb6Uy5eBCSLic5cPwt5o\nHBCxz0HAdxHB5RfYL+6ngR9jb5L+JCK/p6NS4H5sldcR2JuXDkTkNGxw+aUxpqfpm+8C3sdWB78E\n3Coi+0Qc67iIfB0HfADcHeM492KD1PnAL4Ep2JusSFsDG7DTTR+B/aye7h4/7H+Aqe7gmOE8lAMn\nuOfICH2CyV37YO/U5kesezDBY3xujDnN/f05EdkfGwCuc9dd4P57uDFmE4CIfAIswxbcR+I8z6XA\nImPM2RHnGoK9i9zMGLN5RF33S2Yx9u7wGOABY8xGEXkc++G6z90v/CGK/kJRmdGXcvkKUA7sgX1K\nORAbFP5LRHYxxqxw170CICJeYDZwnzEmXFafd29ILhaRm40xAXd9CfbJ+6nwydwng/DvvwL+DJxq\njPlbHHl9xBjzBzftS9jgdjxbAtclwLPGmHMi8rU1EP4MICJjsE9VJ4Wvl4i8CHyBfZoBwBjzIRBZ\nG/Aa0AjcIyIz3KeYR7DBfSpbAsp/An7g4Tj+npTQJ5jc9R7wWxGZLiI79/IYz0ctf4x9igjbB3g+\nHFwAjDFvAJ/T8a6yS26g2AN4KmrTEzH23U9EFovIeqAdaMJ+4UT+fXcDB4rI993l/8TeKGXsQ6Q6\n6HW5NMYY7A3Fge6qg4D/Bd6JWHcAboDBltVtsU8tkeYDFcDYiHWOe6xYzgNuxn7RxxNcIOKzY4xp\nA1a6+QmX+d2xTy+Ropf3jl7vVv0ujtxJRDwicr6IfCwizUAb9mm+CFstiPsZfQz7NB92GvC0MSZj\nHW40wOSuc4F/AJcDRkRWuvXHidgYtdwKFEcsbwPEamv5DluvHI8h2ACwJmp9h2URGYX90HqwVXX7\nYz+Aa6Ly9BLwL7Z8kE4HnjLGbIgzPyq1+louX8HeQGyH/fJ8NWLdaGx5CgeY8BNIdBkNL0eW0dpw\ne0UMJwCfYttH4tXdZydc5tdG7RO9PByoN8Y0R62P/qycj20HfRL7NL8Ptv0HOn42wjdfO4rIjtig\nHKsqOm00wOQoY8xGY8x5xpjhwDhstdVDIvKDJJ7mG2BojPXDsHXC8ViLfRqJPk708hHYevJjjDGP\nGWOqsXfDHQKZMcbBfmhOFZEq7B1txuqYVUdJKJevYP9PDwI+du++X8F+WR6ErToKt7V94/4bXZbC\nUzBHltHu2oFOBsqABSKSjO7W4TI/JGp99PK3wIAY54z+e6YCjxpjLjXGPG+MeRNbRdaBMeZl7JPU\nz7E3YF/TuZYirTTA5AG3cfu32P/PXZJ46GXA4SIyILxCRPYGdsDeWcaTtyA2UBwTten4qOUSIIT9\nYIaFq7+i3YetjrgH+IqoKgWVHXpZLl/BfhH/EtvoH163PbajSbVbpsB2H/4a+wUc6T+xgejDOM/5\nb+BQoAp4TET8caaLqZsy/x9Ry29Gr3fbFA+L2q8E24Mz0sldnP4ebIA5FdtuGexiv7TQRv4cJSKv\nYh+ZP8Lenf0Ce1fTqXdMH9yIbZR8TkT+iG0PuRb7wX08geNcAzwhIre7eZ6EfWKJ9H/YXmP3isjd\nwBhsw2Z0VQTGmK9FZBFwNDAn0x8itUUSyuV72OBwEHA7gDFmg4h87K67NLyjMSbkdiO+w223W4wt\nW2cDl0Q08PfIGPMvEZmMDWoPishPjTGheNPHEC7zt2DbWPbHllewN1IYY2pE5GngdhGpwD6R/Rbb\n9hhpMXCeiCwDVmGDy07Edj/wB+x3+319yH9S6BNM7lqKfQx+DNsnfmvgSPfFsKQwxqzF9vcPYHup\n3Iq9mzysm/rsWMd5EtvF+cfY+vk9gP+K2udDbHvKvtjuzNOwd6Z1XRz2H+6/Wj2WXfpULt0v9Wp3\n8apcNlkAAAFLSURBVOWITeF2l1ej9r8L20h/HLbc/BS4wBhzbaIZN8Ysx3YTPhy4y30hs1fcMn8e\ntpfYP7DtieGeYJsidj0NW411M7YN5Z9AdEeDK7Gfvz+4/7a6x4513m+xNQ+vuZ0mMkqnTFY5SUT+\njn3h8sAed1YqC4jIZdgnsMoYDfvJOkclttr4XGNMrPdu0kqryFROEZGxwF7YNpxEe80plRbue14X\nY9/6b8J2UvgdcHcqgovbTvoD7IvS9cT/jlpKaYDJQyLS3f+rk+NtFguw1S63GWMey3RmVPzyvFxG\na8V2bDgVGIhtX/kzMCtF5xuPDWarsS+LRrfjZIRWkeUhEenuP3WJMebgdOVFqTAtl/2PPsHkp727\n2Vaftlwo1ZGWy35Gn2CUUkqlhHZTVkoplRIaYJRSSqWEBhillFIpoQFGKaVUSmiAUUoplRL/Dxsa\nzxAQsaW1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0162f9518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)=plt.subplots(ncols=2)\n",
    "#print(hour_df['is_holiday'])\n",
    "sn.barplot(data=data[['is_holiday','total_count','season']],x='is_holiday',y='total_count',hue='season',ax=ax1)\n",
    "sn.barplot(data=data[['is_workingday','total_count','season']],x='is_workingday',y='total_count',hue='season',ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0161342e8>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CiPSwpr8sn21Jpy6EyHVkpC6EEG5EOnUhhHAjmVV+UUoVxfjOTAcgHHhTa/29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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa01625f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = data[[\"temp\",\"atemp\",\n",
    "                    \"humidity\",\"windspeed\",\n",
    "                    \"casual\",\"registered\",\n",
    "                    \"total_count\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aOC9cDiYTD5YuxPzfXRyq1aTQgMGWh7xGw8O//8B449rL1ZeaxEyZMud1wCil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EAAAQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa021804828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=data.columns\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr=data.corr().abs()\n",
    "data_corr.shape\n",
    "sn.heatmap(data_corr,annot=True)\n",
    "sn.heatmap(data_corr,mask=data_corr<1,cbar=False)\n",
    "plt.savefig('sharebikes_coor.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.9917015532294651, 1, 2], [0.945516915809036, 6, 7], [0.6728044333386828, 5, 7], [0.6596228675645851, 0, 6], [0.6310656998491813, 2, 7], [0.6288302722083061, 0, 7], [0.6274940090334917, 1, 7], [0.544191757638015, 2, 6], [0.5438636902622045, 2, 5], [0.5432846616821879, 1, 5], [0.5400119661617868, 1, 6]]\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "print(s_corr_list)\n",
    "\n",
    "#Print correlations and column names\n",
    "# for v,i,j in s_corr_list:\n",
    "#     print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa015b5be10>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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49X7JXXBEdmbwmm++S4EvAq8MJS+FaiyNVvMqt0apYW+zzTY899xz8XAj5DkpTRh7F/BG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa016319e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)= plt.subplots(ncols=2)\n",
    "sn.boxplot(data=data[['total_count',\n",
    "                         'casual','registered']],ax=ax1)\n",
    "sn.boxplot(data=data[['temp','windspeed']],ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4jrX9/QjyYo02c2IGAaQppTKBNq2118r7lFLqX0Al8HPgqUgugLDHYx9tY0+D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\nixBCiKj7/90q2a3daAzxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa015be5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sn.distplot(data['total_count'],bins=30,kde=True)\n",
    "plt.xlabel('Media value of total_count')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 对类别特征进行独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(584, 12)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data['total_count'].values\n",
    "X=data.drop(['total_count','casual','registered','datetime'],axis=1)\n",
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=22, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "# ss_X = StandardScaler()\n",
    "# ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# X_train = ss_X.fit_transform(X_train)\n",
    "# X_test = ss_X.transform(X_test)\n",
    "\n",
    "#数据归一化\n",
    "X_train = preprocessing.normalize(X_train, norm='l2')\n",
    "X_test = preprocessing.normalize(X_test, norm='l2')\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "# y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "# y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.43828734301195726\n",
      "0.46166794389865906\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.315102e+06</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.399709e+06</td>\n",
       "      <td>year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.564645e+05</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.726089e+03</td>\n",
       "      <td>month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.358268e+03</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-5.146662e+03</td>\n",
       "      <td>is_workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.543643e+03</td>\n",
       "      <td>rec_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.937038e+04</td>\n",
       "      <td>humidity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2.310201e+04</td>\n",
       "      <td>weather_condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-8.982040e+04</td>\n",
       "      <td>is_holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-6.470322e+05</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-3.291712e+06</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coef            columns\n",
       "8   3.315102e+06               temp\n",
       "2   1.399709e+06               year\n",
       "1   1.564645e+05             season\n",
       "3   2.726089e+03              month\n",
       "5   2.358268e+03            weekday\n",
       "6  -5.146662e+03      is_workingday\n",
       "0  -7.543643e+03             rec_id\n",
       "10 -1.937038e+04           humidity\n",
       "7  -2.310201e+04  weather_condition\n",
       "4  -8.982040e+04         is_holiday\n",
       "11 -6.470322e+05          windspeed\n",
       "9  -3.291712e+06              atemp"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(hour_df)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg=LinearRegression()\n",
    "\n",
    "columns=X.columns\n",
    "#y_train_log=np.log1p(y_train)\n",
    "\n",
    "lin_reg.fit(X=X_train,y=y_train)\n",
    "\n",
    "y_test_predict=lin_reg.predict(X_test)\n",
    "y_train_predict=lin_reg.predict(X_train)\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "print(r2_score(y_test,y_test_predict)) #测试集\n",
    "print(r2_score(y_train,y_train_predict)) #训练集\n",
    "\n",
    "#用于后续显示权重系数对\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lin_reg.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jPCpv3kRgDrA58AfgqBjjwqL3VJKkClPoSPh1YDrw0/yJIYSNgHnAOUAt8Ajw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\nfO91bKwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa02fb958d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_predict,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Plx2T3+fyq8T6OSUqiXw9ThXMrcAM4D20/wvoqOrRwBvA3fEX7SAiMgqYChQD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa016583b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_predict)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.1, 'max_iter': 3000}\n",
      "RMSLE Value for Ridge Regressioon: 0.505906915920875\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:747: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa015cfd208>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tOPf/L6q27QpgWUMHsNYecfet2b4Y5wHDk69vqOcYHwPG56pFpElyuVxs2HeU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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa015c9dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import metrics,svm\n",
    "\n",
    "def rmsle(y,y_,convertExp=True):\n",
    "    if convertExp:\n",
    "        y=np.exp(y)\n",
    "        y_=np.exp(y_)\n",
    "    #print(y_)\n",
    "    log1=np.nan_to_num(np.array([np.log(v+1) for v in y]))\n",
    "    #print(log1)\n",
    "    log2=np.nan_to_num(np.array([np.log(v+1) for v in y_]))\n",
    "    calc=(log1-log2)**2\n",
    "    return np.sqrt(np.mean(calc))\n",
    "\n",
    "ridge_m_ = Ridge()\n",
    "ridge_params_={'max_iter':[3000],'alpha':[0.1,1,2,3,4,10,30,100]}\n",
    "rmsle_scorer=metrics.make_scorer(rmsle,greater_is_better=False)\n",
    "grid_ridge_m=GridSearchCV(ridge_m_,ridge_params_,scoring=rmsle_scorer,cv=5)\n",
    "#print(grid_ridge_m)\n",
    "#print(yLabels)\n",
    "y_train_log=np.log1p(y_train)\n",
    "#print(yLabelslog)\n",
    "#print(dataTrain)\n",
    "grid_ridge_m.fit(X_train,y_train_log)\n",
    "preds=grid_ridge_m.predict(X=X_train)\n",
    "print(grid_ridge_m.best_params_)\n",
    "print(\"RMSLE Value for Ridge Regressioon:\",rmsle(np.exp(y_train_log),np.exp(preds),False))\n",
    "fig,ax=plt.subplots()\n",
    "fig.set_size_inches(8,6)\n",
    "df=pd.DataFrame(grid_ridge_m.grid_scores_)\n",
    "df['alpha']=df['parameters'].apply(lambda x:x['alpha'])\n",
    "df['rmsle']=df['mean_validation_score'].apply(lambda x:-x)\n",
    "sn.pointplot(data=df,x='alpha',y='rmsle',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.001, 'max_iter': 3000}\n",
      "RMSLE Value For Lasso Regression:  0.511629639419841\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:747: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0158c4b70>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7EOhoZrMiywOAR4DjgZeAq8ysvJFNSJZ6eOE69lTWHlwuzMtl2id0skVERESy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\nmy9H2lyIN17y7jhtvoX3oXZg7Nljcb5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa015ed5ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso_m_=Lasso()\n",
    "alpha=1/np.array([0.1,1,2,3,4,10,30,100,1000])\n",
    "lasso_params_={'max_iter':[3000],'alpha':alpha}\n",
    "grid_lasso_m=GridSearchCV(lasso_m_,lasso_params_,scoring=rmsle_scorer,cv=5)\n",
    "y_train_log=np.log1p(y_train)\n",
    "grid_lasso_m.fit(X_train,y_train_log)\n",
    "preds=grid_lasso_m.predict(X=X_train)\n",
    "print(grid_lasso_m.best_params_)\n",
    "print('RMSLE Value For Lasso Regression: ',rmsle(np.exp(y_train_log),np.exp(preds),False))\n",
    "fig,ax=plt.subplots()\n",
    "fig.set_size_inches(12,5)\n",
    "df=pd.DataFrame(grid_lasso_m.grid_scores_)\n",
    "df['alpha']=df['parameters'].apply(lambda x:x['alpha'])\n",
    "df['rmsle']=df['mean_validation_score'].apply(lambda x: -x)\n",
    "sn.pointplot(data=df,x='alpha',y='rmsle',ax=ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.21560068088485074\n",
      "The r2 score of RidgeCV on train is 0.2683910713860206\n"
     ]
    }
   ],
   "source": [
    "#岭回归/L2正则\n",
    "from sklearn.linear_model import RidgeCV\n",
    "alphas=[0.01,0.1,1,10,100]\n",
    "ridge=RidgeCV(alphas=alphas,store_cv_values=True)\n",
    "\n",
    "ridge.fit(X_train,y_train)\n",
    "\n",
    "#预测\n",
    "y_test_predict_ridge=ridge.predict(X_test)\n",
    "\n",
    "y_train_predict_ridge=ridge.predict(X_train)\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_predict_ridge))\n",
    "print('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_predict_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa015691390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.315102e+06</td>\n",
       "      <td>-465.256097</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.399709e+06</td>\n",
       "      <td>2521.725601</td>\n",
       "      <td>year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.564645e+05</td>\n",
       "      <td>-622.418303</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.726089e+03</td>\n",
       "      <td>-21760.379971</td>\n",
       "      <td>month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.358268e+03</td>\n",
       "      <td>-7315.972250</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-5.146662e+03</td>\n",
       "      <td>5616.245235</td>\n",
       "      <td>is_workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.543643e+03</td>\n",
       "      <td>-13264.371549</td>\n",
       "      <td>rec_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.937038e+04</td>\n",
       "      <td>-4152.121167</td>\n",
       "      <td>humidity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2.310201e+04</td>\n",
       "      <td>-13121.776680</td>\n",
       "      <td>weather_condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-8.982040e+04</td>\n",
       "      <td>-823.147321</td>\n",
       "      <td>is_holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-6.470322e+05</td>\n",
       "      <td>-1252.105673</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-3.291712e+06</td>\n",
       "      <td>-632.115077</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         coef_lr    coef_ridge            columns\n",
       "8   3.315102e+06   -465.256097               temp\n",
       "2   1.399709e+06   2521.725601               year\n",
       "1   1.564645e+05   -622.418303             season\n",
       "3   2.726089e+03 -21760.379971              month\n",
       "5   2.358268e+03  -7315.972250            weekday\n",
       "6  -5.146662e+03   5616.245235      is_workingday\n",
       "0  -7.543643e+03 -13264.371549             rec_id\n",
       "10 -1.937038e+04  -4152.121167           humidity\n",
       "7  -2.310201e+04 -13121.776680  weather_condition\n",
       "4  -8.982040e+04   -823.147321         is_holiday\n",
       "11 -6.470322e+05  -1252.105673          windspeed\n",
       "9  -3.291712e+06   -632.115077              atemp"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lin_reg.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.23016598900948948\n",
      "The r2 score of LassoCV on train is 0.25295317027864006\n"
     ]
    }
   ],
   "source": [
    "### Lasso/L1正则\n",
    "from sklearn.linear_model import LassoCV\n",
    "lasso=LassoCV()\n",
    "lasso.fit(X_train,y_train)\n",
    "y_test_predict_lasso=lasso.predict(X_test)\n",
    "y_train_predict_lasso=lasso.predict(X_train)\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print('The r2 score of LassoCV on test is', r2_score(y_test, y_test_predict_lasso))\n",
    "print('The r2 score of LassoCV on train is', r2_score(y_train, y_train_predict_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SAgAuPbE/d501XBJWCzwtrfpGGfouQpskLWFbWw8c4Y73jNkuJg1M449nZ0vCOoZuybGM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\nSVpCCCGChiQtIYQQQUOSlhBCiKDx/wHlKnskXOmgSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa015675e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 3.154178048834227\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>3.315102e+06</td>\n",
       "      <td>-465.256097</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.399709e+06</td>\n",
       "      <td>2521.725601</td>\n",
       "      <td>year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.564645e+05</td>\n",
       "      <td>-622.418303</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-28528.238405</td>\n",
       "      <td>2.726089e+03</td>\n",
       "      <td>-21760.379971</td>\n",
       "      <td>month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-5256.900193</td>\n",
       "      <td>2.358268e+03</td>\n",
       "      <td>-7315.972250</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.146662e+03</td>\n",
       "      <td>5616.245235</td>\n",
       "      <td>is_workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7324.421138</td>\n",
       "      <td>-7.543643e+03</td>\n",
       "      <td>-13264.371549</td>\n",
       "      <td>rec_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-1.937038e+04</td>\n",
       "      <td>-4152.121167</td>\n",
       "      <td>humidity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-5058.547415</td>\n",
       "      <td>-2.310201e+04</td>\n",
       "      <td>-13121.776680</td>\n",
       "      <td>weather_condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-8.982040e+04</td>\n",
       "      <td>-823.147321</td>\n",
       "      <td>is_holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-6.470322e+05</td>\n",
       "      <td>-1252.105673</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-3.291712e+06</td>\n",
       "      <td>-632.115077</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      coef_lasso       coef_lr    coef_ridge            columns\n",
       "8      -0.000000  3.315102e+06   -465.256097               temp\n",
       "2       0.000000  1.399709e+06   2521.725601               year\n",
       "1      -0.000000  1.564645e+05   -622.418303             season\n",
       "3  -28528.238405  2.726089e+03 -21760.379971              month\n",
       "5   -5256.900193  2.358268e+03  -7315.972250            weekday\n",
       "6       0.000000 -5.146662e+03   5616.245235      is_workingday\n",
       "0   -7324.421138 -7.543643e+03 -13264.371549             rec_id\n",
       "10     -0.000000 -1.937038e+04  -4152.121167           humidity\n",
       "7   -5058.547415 -2.310201e+04 -13121.776680  weather_condition\n",
       "4      -0.000000 -8.982040e+04   -823.147321         is_holiday\n",
       "11     -0.000000 -6.470322e+05  -1252.105673          windspeed\n",
       "9      -0.000000 -3.291712e+06   -632.115077              atemp"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lin_reg.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
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 "nbformat_minor": 2
}
